Control of the molecular behavior of SHIP by FcγRIIB and Nck (P1122)
Bibliographic record
Abstract
Abstract Leukocyte activity is controlled by balancing activating and inhibitory signals in order to generate effective immune responses while avoiding tissue damage and autoimmunity. This project examines an important regulatory circuit in B cells that involves the lipid phosphatase SHIP. Localization, phosphorylation, and binding to interaction partners are important factors in the regulation of this protein, however knowledge of their inter-relationships is incomplete. Here, we examine the influence of two interaction partners on the molecular behaviour and function of SHIP. First, we examine in detail how the inhibitory receptor FcγRIIB controls SHIP localization dynamics. We have observed that co-engagement of FcγRIIB along with the B cell receptor in A20 cells does not influence the magnitude or kinetics of SHIP-EGFP recruitment to the membrane as assessed by confocal microscopy, however it does alter mobility at the cell periphery as measured by fluorescence recovery after photobleaching. Next, we probe the influence of a novel binding partner, the adaptor protein Nck. We have demonstrated an interaction between SHIP and Nck by both Biacore affinity analysis and pull-down assays. Functional relevance will be addressed through mutagenesis experiments. With these aims we hope to enhance our understanding of an interaction that is already recognized as relevant and investigate the function of a novel interaction that occurs at a previously uncharacterized regulatory site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".